Indoor Navigation and Mapping: Performance Analysis of Uwb-Based Platform Positioning (original) (raw)

UWB Indoor Localization Using Deep Learning LSTM Networks

Applied Sciences, 2020

Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user posit...

A UWB-Based Indoor Positioning System Employing Neural Networks

Journal of Geovisualization and Spatial Analysis

Because of its high time-domain resolution, ultra-wideband (UWB) technology is capable of precise range measurement and has been applied in many accurate indoor positioning systems. This paper presents a practical indoor positioning and navigation system using the UWB modules we developed based on Decawave UWB chipset. In order to enhance the performance of the system in NLOS scenarios, a neural network approach is integrated. The feasibility of the combined approach is analysed and its accuracy is evaluated. The experiment results indicate that the neural network can maintain a stable accuracy no matter whether it is in an NLOS scenario and it is capable to identify the locations on two trajectories which are next to each other with very high confidence.

Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks

IEEE Access, 2022

Accurate localization in indoor environments with ultra-wideband (UWB) technology has long attracted much attention. However, due to the presence of multipath components or non-line of sight (NLOS) propagation of the radio signals, it has been converted to a critical challenge. Existing solutions use many fixed anchors in the indoor environment. Particularly, large areas require many anchor points and in the case of unexpected events that lead to the destruction of existing infrastructures, the fixed anchor points cannot be used. In this paper, a novel localization framework based on the transmitting signal from a mobile UWB sensor on the outside of the building and its received signal regarding the modified Saleh Valenzuela (SV) channel model is presented. After preprocessing the received signals, two new procedures to reduce the ranging error caused by multipath components are proposed. In the first procedure, two machine learning algorithms including multi-layer perceptron (MLP) and support vector machine (SVM) using the extracted features from the received UWB signal time and power vectors are implemented. Moreover, in the second procedure, two deep learning algorithms including MLP and convolutional neural networks (CNNs) using the received UWB signal time and power vectors are implemented to improve the performance of the indoor localization system. The simulation results show that the architecture designed for the convolutional neural network based on the hybrid dataset (the combination of the dataset related to received UWB signal time and power vectors) provides a mean absolute error (MAE) of about 3 cm.

Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances

Sensors, 2016

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

Ultra-Wideband Aided Localization and Mapping System

— This paper proposes an ultra-wideband (UWB) aided localization and mapping pipeline that leverages on inertial sensor and depth camera. Inspired by the fact that visual odometry (VO) system, regardless of its accuracy in the short term, still faces challenges with accumulated errors in the long run or under unfavourable environments, the UWB ranging measurements are fused to remove the visual drift and improve the robustness. A general framework is developed and consists of three parallel threads, two of which carry out the visual-inertial odometry (VIO) and UWB localization respectively. The other mapping thread integrates the visual tracking constraints into a pose graph with the proposed smooth and virtual range constraints, such that a bundle adjustment is performed to provide robust trajectory estimation. Experiments show that the proposed system is able to create dense drift-free maps in real-time even running on an ultra-low power processor in featureless environments.

Ultra-wideband aided fast localization and mapping system

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017

This paper proposes an ultra-wideband (UWB) aided localization and mapping system that leverages on inertial sensor and depth camera. Inspired by the fact that visual odometry (VO) system, regardless of its accuracy in the short term, still faces challenges with accumulated errors in the long run or under unfavourable environments, the UWB ranging measurements are fused to remove the visual drift and improve the robustness. A general framework is developed which consists of three parallel threads, two of which carry out the visualinertial odometry (VIO) and UWB localization respectively. The other mapping thread integrates visual tracking constraints into a pose graph with the proposed smooth and virtual range constraints, such that an optimization is performed to provide robust trajectory estimation. Experiments show that the proposed system is able to create dense drift-free maps in real-time even running on an ultra-low power processor in featureless environments.

Ultra wideband indoor navigation system

IET Radar Sonar & Navigation, 2012

Typical indoor environments contain multiple walls and obstacles consisting of different materials. As a result, current narrowband radio frequency (RF) indoor navigation systems cannot satisfy the challenging demands for most indoor applications. The RF ultra wideband (UWB) system is a promising technology for indoor localisation owing to its high bandwidth that permits mitigation of the multipath identification problem. This work proposes a novel UWB navigation system that permits accurate mobile robot (MR) navigation in indoor environments. The navigation system is composed of two sub-systems: the localisation system and the MR control system. The main contributions of this work are focused on estimation algorithm for localisation, digital implementation of transmitter and receiver and integration of both sub-systems that enable autonomous robot navigation. For sub-systems performance evaluation, statics and dynamics experiments were carried out which demonstrated that the proposed system reached an accuracy that outperforms traditional sensors technologies used in robot navigation, such as odometer and sonar.

Accurate and robust indoor localization systems using ultra-wideband signals

Indoor localization systems that are accurate and robust with respect to propagation channel conditions are still a technical challenge today. In particular, for systems based on range measurements from radio signals, non-line-of-sight (NLOS) situations can result in large position errors. In this paper, we address these issues using measurements in a representative indoor environment. Results show that conventional tracking schemes using high-and a low-complexity ranging algorithms are strongly impaired by NLOS conditions unless a very large signal bandwidth is used. Furthermore, we discuss and evaluate the performance of multipath-assisted indoor navigation and tracking (MINT), that can overcome these impairments by making use of multipath propagation. Across a wide range of bandwidths, MINT shows superior performance compared to conventional schemes, and virtually no degradation in its robustness due to NLOS conditions.

Evaluation of positioning and ranging errors for UWB indoor applications

2019

Nowadays location information is a common requirement for numerous application fields like Location Based Services (LBS), Intelligent Transport Systems (ITS), precise agriculture, augmented reality and more. Most common navigation systems rely upon Global Navigation Satellite System (GNSS) which is by far the most cost-effective outdoor positioning system. Unfortunately, when the operation is moved indoor, the radiofrequency signals broadcasted by the satellites network are not able to achieve the receiver on the earth and the positioning is no longer available. So, dealing with GNSSdenied environment makes it necessary to use alternative solutions to aid navigation. Among the numerous solutions for indoor positioning, Ultra-Wide Band (UWB) systems are particularly interesting due to their signal characteristics. UWB signal allows high accuracy in ranging estimation, it doesn’t interfere with other RF signal like GNSS and Wi-Fi and the hardware it is easily producible and therefore ...

Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation

2011

In this work, a comparative study between an Ultra Wide-Band (UWB) localization system and a Simultaneous Localization and Mapping (SLAM) algorithm is presented. Due to its high bandwidth and short pulses length, UWB potentially allows great accuracy in range measurements based on Time of Arrival (TOA) estimation. SLAM algorithms recursively estimates the map of an environment and the pose (position and orientation) of a mobile robot within that environment. The comparative study presented here involves the performance analysis of implementing in parallel an UWB localization based system and a SLAM algorithm on a mobile robot navigating within an environment. Real time results as well as error analysis are also shown in this work.